Non Convex Loss Function

Non-convex loss functions pose significant challenges in machine learning due to the absence of a globally optimal solution, hindering the development of efficient and robust algorithms. Current research focuses on developing novel algorithms for federated learning, Byzantine-resilient training, and differentially private optimization, often employing techniques like gradient normalization, adaptive client selection, and model sparsification to mitigate the difficulties associated with non-convexity. These advancements are crucial for improving the efficiency, privacy, and robustness of machine learning models in distributed and sensitive data settings, impacting various applications from healthcare to large-scale data analysis.